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Study on Prediction of Similar Typhoons through Neural Network Optimization

뉴럴 네트워크의 최적화에 따른 유사태풍 예측에 관한 연구

  • Kim, Yeon-Joong (Department of Civil and Urban Engineering Inje University) ;
  • Kim, Tae-Woo (Department of Civil and Urban Engineering Inje University) ;
  • Yoon, Jong-Sung (Department of Civil and Urban Engineering Inje University) ;
  • Kim, In-Ho (Department of Earth and Environmental Engineering Kangwon National University)
  • 김연중 (인제대학교 토목도시공학부) ;
  • 김태우 (인제대학교 토목도시공학부) ;
  • 윤종성 (인제대학교 토목도시공학부) ;
  • 김인호 (강원대학교 지구환경시스템공학과)
  • Received : 2019.08.04
  • Accepted : 2019.10.16
  • Published : 2019.10.31

Abstract

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

Keywords

Artificial intelligence;Deep learning;Big data;Activation function;Disaster prevention system

Acknowledgement

Supported by : 해양수산과학기술진흥원, 강원대학교